River Ice Segmentation with Deep Learning
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This paper deals with the problem of computing surface ice concentration for two different types of ice from river ice images. It presents the results of attempting to solve this problem using several state of the art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges - very limited availability of labeled training data and the great difficulty of visually distinguishing the two types of ice, even for humans, leading to noisy labels.. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges.
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